Details of Research Outputs

TitleRegularized LTI System Identification with Multiple Regularization Matrix ⁎
Author (Name in English or Pinyin)
Chen, Tianshi1; Andersen, Martin S.2; Mu, Biqiang3; Yin, Feng1; Ljung, Lennart3; Qin, S. Joe4
Date Issued2018-10-08
Source PublicationIFAC-PapersOnLine
ISSN24058963
DOI10.1016/j.ifacol.2018.09.121
Funding Project国家自然科学基金项目
Firstlevel Discipline信息科学与系统科学
Education discipline科技类
Published range国外学术期刊
Volume Issue Pagesv 51,n 15,p180-185
References
[1] Carli, F.P., Chen, T., Ljung, L., Maximum entropy kernels for system identification. IEEE Transactions on Automatic Control 62:3 (2017), 1471–1477.
[2] Chen, T., Andersen, M.S., Ljung, L., Chiuso, A., Pillonetto, G., System identification via sparse multiple kernel-based regularization using sequential convex optimization techniques. IEEE Transactions on Automatic Control(11), 2014, 2933–2945.
[3] Chen, T., Ohlsson, H., Ljung, L., On the estimation of transfer functions, regularizations and Gaussian processes - Revisited. Automatica 48 (2012), 1525–1535.
[4] Chen, T., On kernel design for regularized LTI system identification. Automatica 90 (2018), 109–122.
[5] Chen, T., Continuous-time DC kernel - a stable generalized first-order spline kernel. IEEE Transactions on Automatic Control, 2019.
[6] Chen, T., Ardeshiri, T., Carli, F.P., Chiuso, A., Ljung, L., Pillonetto, G., Maximum entropy properties of discrete-time first-order stable spline kernel. Automatica 66 (2016), 34–38.
[7] Chen, T., Pillonetto, G., On the stability of reproducing kernel hilbert spaces of discrete-time impulse responses. Automatica, 2018.
[8] Chiuso, A., Regularization and Bayesian learning in dynamical systems: Past, present and future. Annual Reviews in Control 41 (2016), 24–38.
[9] Hastie, T., Tibshirani, R., Friedman, J., The Elements of Statistical Learning, 2001, Springer.
[10] Hong, S., Mu, B., Yin, F., Andersen, M.S., and Chen, T. (2018). Multiple kernel based regularized system identification with SURE hyper-parameter estimator. In The 18th IFAC Symposium on System Identification (SYSID).
[11] Hunter, D.R., Lange, K., A tutorial on MM algorithms. American Statistician 58 (2004), 30–37.
[12] Latarie, J., Chen, T., Transfer function and transient estimation by Gaussian process regression in frequency domain. Automatica 52 (2016), 217–229.
[13] Ljung, L., System Identification - Theory for the User, 2nd edition, 1999, Prentice-Hall, Upper Saddle River, N.J.
[14] Marconato, A., Schoukens, M., Schoukens, J., Filter-based regularisation for impulse response modelling. IET Control Theory & Applications 11 (2016), 194–204.
[15] Mu, B., Chen, T., and Ljung, L. (2018a). Asymptotic properties of generalized cross validation estimators for regularized system identification. In The 18th IFAC Symposium on System Identification (SYSID).
[16] Mu, B. and Chen, T. (2018). On input design for regularized LTI system identification: Power-constrained input. Automatica, revised in January 2018, available from http://arxiv.org/abs/1708.05539.
[17] Mu, B., Chen, T., and Ljung, L. (2017). On the input design for kernel-based regularized LTI system identification: Power-constrained inputs. Proc. 56th IEEE Conference on Decision and Control.
[18] Mu, B., Chen, T., Ljung, L., On asymptotic properties of hyperparameter estimators for kernelbased regularization methods. Automatica, 2018.
[19] Pillonetto, G., Dinuzzo, F., Chen, T., De Nicolao, G., Ljung, L., Kernel methods in system identification. machine learning and function estimation: A survey. Automatica 50:3 (2014), 657–682.
[20] Pillonetto, G., Nicolao, G.D., A new kernelbased approach for linear system identification. Automatica 46:1 (2010), 81–93.
[21] Pillonetto, G., Chiuso, A., Tuning complexity in regularized kernel-based regression and linear system identification: The robustness of the marginal likelihood estimator. Automatica 58 (2015), 106–117.
[22] Prando, G., Chiuso, A., Pillonetto, G., Maximum entropy vector kernels for MIMO system identification. Automatica 79 (2017), 326–339.
[23] Yuille, A.L., Rangarajan, A., The concaveconvex procedure (CCCP). Advances in Neural Information Processing Systems 2 (2002), 1033–1040.
[24] Zorzi, M. and Chiuso, A. (2017). The harmonic analysis of kernel functions. ArXiv preprint arXiv:1703.05216.
Citation statistics
Cited Times [WOS]:0   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
Identifierhttps://irepository.cuhk.edu.cn/handle/3EPUXD0A/130
CollectionShenzhen Research Institute of Big Data
School of Data Science
School of Science and Engineering
Affiliation
1.School of Science and Engineering and Shenzhen Research Institute of Big Data, The Chinese University of Hong Kong, Shenzhen, Hong Kong
2.Department of Applied Mathematics and Computer Science, Technical University of Denmark, Copenhagen, Denmark
3.Department of Electrical Engineering, Linköping University, Linköping, Sweden
4.Department of Electrical Engineering, Department of Chemical Engineering and Materials Science, University of Southern California, United States
First Author AffilicationShenzhen Research Institute of Big Data
Recommended Citation
GB/T 7714
Chen, Tianshi,Andersen, Martin S.,Mu, Biqianget al. Regularized LTI System Identification with Multiple Regularization Matrix ⁎[J]. IFAC-PapersOnLine,2018.
APA Chen, Tianshi, Andersen, Martin S., Mu, Biqiang, Yin, Feng, Ljung, Lennart, & Qin, S. Joe. (2018). Regularized LTI System Identification with Multiple Regularization Matrix ⁎. IFAC-PapersOnLine.
MLA Chen, Tianshi,et al."Regularized LTI System Identification with Multiple Regularization Matrix ⁎".IFAC-PapersOnLine (2018).
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